Global Difference-Aware Mamba for Hyperspectral Image Change Detection

Deep learning-based methods have been widely applied to hyperspectral image change detection (HSI-CD), where most existing approaches adopt a patch-wise framework by taking paired patches from bi-temporal HSIs as input and determining whether the central position has changed. However, these methods suffer from several limitations. Being patch-wise, they fail to fully exploit the rich information contained in the entire bi-temporal HSIs and often overlook useful content outside the training samples. Although some methods incorporate advanced modeling techniques, their receptive fields are still constrained by the patch size, which limits their ability to capture global dependencies. Besides, many approaches underutilize the inherent advantage of HSI in capturing subtle spectral differences. To address these challenges, we propose a Mamba-based image-level global difference-aware network (GDAMamba) for HSI-CD, consisting of three key modules: the global contextual difference awareness module (GCDAM), the temporal spectral difference enhancement module (TSDEM), and the adaptive gated fusion module (AGFM). Specifically, GCDAM enables global spatial contextual awareness by establishing associations between each pixel and all others on the difference map. TSDEM processes the bi-temporal HSIs in a pixel-wise manner, progressively extracts and enhances spectral differences. These differences are then integrated into the global difference representations through AGFM, which are subsequently fed into a segmentation head to generate the final change detection map. Experimental results on three benchmark datasets demonstrate that GDAMamba outperforms several state-of-the-art methods. Our code is available at https://github.com/Preston-Dong/GDAMamba.